Detection of cores: Algorithmic detection of core periphery introduced by [1]} is one of the most promising new area in network analysis. Several works such as [2]}, [3]} have presented that automatic techniques of separating the core nodes from the sparse periphery.
Batagelj and Zaversnik [4]} proposed a \(k\) -core decomposition algorithm that requires \(O(max(|E|, |V|))\)  runtime and \(O(|E|)\)  space. In [5]}, [6]} the authors proposed modifications of the previous linear approach which scales the computation to millions of nodes and billions of edges. In many networked systems, we are only concerned with estimating the importance of a subset of nodes instead of the entire network. In [7]}, the authors proposed a computation technique for computing the coreness for a node \(u\)  which only takes into account its \(\delta \)  neighborhood. Model based approaches have been presented in [3]}, [9]} where the authors designed objective functions to estimate the coreness of nodes.
